小编Joã*_*tes的帖子

我正在尝试使用Android我的Kivy应用程序创建一个包,buildozer但是当我尝试包含以下内容时,我收到此错误numpy:

恢复错误:

compile options: '-DNO_ATLAS_INFO=1 -Inumpy/core/include -Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -Inumpy/core/include -I/home/joao/github/buildozer/.buildozer/android/platform/python-for-android/build/python-install/include/python2.7 -Ibuild/src.linux-x86_64-2.7/numpy/core/src/multiarray -Ibuild/src.linux-x86_64-2.7/numpy/core/src/umath -c'
ccache: numpy/linalg/lapack_litemodule.c
ccache: numpy/linalg/python_xerbla.c
/usr/bin/gfortran -Wall -lm build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib -L/home/joao/github/buildozer/.buildozer/android/platform/python-for-android/build/python-install/lib -Lbuild/temp.linux-x86_64-2.7 -llapack -lblas -lpython2.7 -lgfortran -o build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so
/usr/bin/ld: build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o: Relocations in generic ELF (EM: 40)
/usr/bin/ld: build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o: Relocations in generic ELF (EM: 40)
build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o: error adding symbols: File in wrong format
collect2: error: ld returned 1 exit status
/usr/bin/ld: build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o: Relocations in …

在我的笔记本Jupyter我现在使用%matplotlib notebook的替代%matplotlib inline,它的真棒,我现在可以用我的Jupyter地块互动.但是,当我尝试制作直方图时,我得到一个空白的情节:

如果我使用%matplotlib inline一切正常:

这是怎么回事？

我试图在Windows上运行CMake,我收到以下错误:

-- The C compiler identification is unknown
CMake Error at CMakeLists.txt:3 (PROJECT):
  The CMAKE_C_COMPILER:

    cl

  is not a full path and was not found in the PATH.

  To use the NMake generator with Visual C++, cmake must be run from a shell
  that can use the compiler cl from the command line.  This environment is
  unable to invoke the cl compiler.  To fix this problem, run cmake from the
  Visual Studio Command Prompt (vcvarsall.bat).

  Tell CMake where to find …

我正在尝试更新firebase_messaging到新版本8.0.0-dev.10。

但是，我收到以下错误：

Running Gradle task 'assembleDebug'...
Application.kt: (5, 27): Unresolved reference: firebasemessaging
Application.kt: (6, 27): Unresolved reference: firebasemessaging
Application.kt: (11, 9): Unresolved reference: FlutterFirebaseMessagingService
Application.kt: (15, 9): Unresolved reference: FirebaseMessagingPlugin

这就是我的 Application.kt 的样子

Running Gradle task 'assembleDebug'...
Application.kt: (5, 27): Unresolved reference: firebasemessaging
Application.kt: (6, 27): Unresolved reference: firebasemessaging
Application.kt: (11, 9): Unresolved reference: FlutterFirebaseMessagingService
Application.kt: (15, 9): Unresolved reference: FirebaseMessagingPlugin

有任何想法吗？

我有一个size = N的向量,其中每个元素我可以有从0到possible_values [i] -1的值.我想做一个函数,迭代我所有这些值.

我能够使用递归生成器在Python中执行此操作:

def all_values(size,values,pos=0):
    if pos == size:
        yield []
    else:    
        for v in xrange(values[pos]):
            for v2 in all_values(size,values,pos+1):
                v2.insert(0,v)
                yield v2

possible_values=[3,2,2]
for v in all_values(3,possible_values):
    print v

示例输出:

[0, 0, 0]
[0, 0, 1]
[0, 1, 0]
[0, 1, 1]
[1, 0, 0]
[1, 0, 1]
[1, 1, 0]
[1, 1, 1]
[2, 0, 0]
[2, 0, 1]
[2, 1, 0]
[2, 1, 1]

由于C++没有Python的产量,我不知道在C++中实现它的正确方法是什么.

可选问题:有没有更好的方法在Python中实现它？

我正在尝试在弹性beanstalk中开始后台工作,后台作业有一个无限循环所以它永远不会返回响应,所以我收到这个错误:"有些实例没有响应命令.没有从[i-ba5fb2f7收到响应]".

我正在弹性beanstalk .config文件中启动后台作业,如下所示:06_start_workers:command:"./ workers.py&"

有没有办法做到这一点？我不希望弹性beanstalk等待该进程的返回值.

我正在尝试从amazon s3提供gzip文件.这是我的settings.py:

AWS_IS_GZIPPED = True
AWS_PRELOAD_METADATA = True 
DEFAULT_FILE_STORAGE = 'storages.backends.s3boto.S3BotoStorage'
STATICFILES_STORAGE = 'storages.backends.s3boto.S3BotoStorage'
AWS_STORAGE_BUCKET_NAME = 'elasticbeanstalk-eu-west-1-2051565523'
STATIC_URL = 'https://%s.s3.amazonaws.com/' % AWS_STORAGE_BUCKET_NAME
COMPRESS_OFFLINE = True
COMPRESS_ENABLED = True
COMPRESS_URL = STATIC_URL
COMPRESS_CSS_FILTERS = [
    'compressor.filters.css_default.CssAbsoluteFilter',
    'compressor.filters.cssmin.CSSMinFilter'
]
COMPRESS_JS_FILTERS = [
    'compressor.filters.jsmin.JSMinFilter',
]
COMPRESS_STORAGE = 'compressor.storage.GzipCompressorFileStorage' 

当我这样做时,django为每个*.js和*.css压缩创建*.gz文件,但奇怪的是只有*.css文件作为gzip提供.我可以在aws s3上看到.css文件有Content-Encoding:gzip而*.js没有.这里发生了什么？

有没有办法在 Sympy 中混合矩阵符号和标量符号？

我想创建一个采用向量和标量的标量函数，例如：

import sympy as sy
v=sy.MatrixSymbol('v',3,1)
f=v.T*v+5

我收到一条错误消息：TypeError：矩阵和标量符号的混合

我知道我可以为我要做的每个点积使用一个符号，但这不是很优雅。

我试图在pyBrain上进行最简单的回归,但不知怎的,我失败了.

神经网络应该学习Y = 3*X的函数

from pybrain.supervised.trainers import BackpropTrainer
from pybrain.datasets import SupervisedDataSet
from pybrain.structure import FullConnection, FeedForwardNetwork, TanhLayer, LinearLayer, BiasUnit
import matplotlib.pyplot as plt
from numpy import *

n = FeedForwardNetwork()
n.addInputModule(LinearLayer(1, name = 'in'))
n.addInputModule(BiasUnit(name = 'bias'))
n.addModule(TanhLayer(1,name = 'tan'))
n.addOutputModule(LinearLayer(1, name = 'out'))
n.addConnection(FullConnection(n['bias'], n['tan']))
n.addConnection(FullConnection(n['in'], n['tan']))
n.addConnection(FullConnection(n['tan'], n['out']))
n.sortModules()

# initialize the backprop trainer and train
t = BackpropTrainer(n, learningrate = 0.1, momentum = 0.0, verbose = True)

#DATASET
DS = SupervisedDataSet( 1, 1 )
X = …

我有以下维度的这些变量:

A   - (3,)
B   - (4,)
X_r - (3,K,N,nS)
X_u - (4,K,N,nS)
k - (K,)

我想计算(A.dot(X_r[:,:,n,s])*B.dot(X_u[:,:,n,s])).dot(k)每一个可能的n和s,我现在正在做的方式如下:

np.array([[(A.dot(X_r[:,:,n,s])*B.dot(X_u[:,:,n,s])).dot(k) for n in xrange(N)] for s in xrange(nS)]) #nSxN

但这是超级慢,我想知道是否有更好的方法,但我不确定.

然而,我正在做另一个计算,我相信它可以被优化:

np.sum(np.array([(X_r[:,:,n,s]*B.dot(X_u[:,:,n,s])).dot(k) for n in xrange(N)]),axis=0)

在这一个我创建一个numpy数组只是为了在一个轴上求和并丢弃后的数组.如果这是1-DI中的列表将使用reduce和优化它,我应该使用什么numpy数组？